A Simulation-Based Evaluation Approach
for Digitalization Scenarios in Smart Supply
Chain Risk Management
F. Schl¨uter1,∗
, E. Hetterscheid1
and M. Henke2
1
Graduate School of Logistics, Technical University of Dortmund,
Dortmund, Germany
2
Chair of Enterprise Logistics, Technical University of Dortmund,
Dortmund, Germany
E-mail: {schlueter; hetterscheid}@gsoflog.de; henke@lfo.tu-dortmund.de
∗
Corresponding Author
Received 28 July 2017; Accepted 23 September 2017;
Publication 25 October 2017
Abstract
To increase the security of modern, volatile supply chains a proactive risk
management based on real-time risk related information transparency is
required. At this time, none or only limited empirical/objective information
about digitalization benefits for supply chain risk management is available.
A method is needed, which draws conclusion on the estimation of costs and
benefits of digitalization initiatives. The paper presents a flexible simulation-
based approach for evaluating digitalization scenarios prior to realization. The
evaluation approach is integrated into a framework and its applicability will
be shown in a case study of a German steel producer, evaluating digitalization
effects on the Mean Lead time-at-risk.
Keywords: Supply chain risk management, Smart supply chain risk
management, Digitalization, Simulation, Industry 4.0.
Journal of Industrial Engineering and Management Science, Vol. 1, 179–206.
doi: 10.13052/jiems2446-1822.2017.009
This is an Open Access publication. c 2017 the Author(s). All rights reserved.

180 F. Schl¨uter et al.
1 Introduction
Several industrial trends (e.g. outsourcing, just-in-time deliveries, shorter
product life cycles) led to an increase in supply chain (SC) vulnerability over
the last years [51]. This can be seen in many example cases, like Ericsson
[12, 51], Toyota [55], Land Rover [67] and other Japanese automotive compa-
nies and computer manufacturer [13].Asupply chain disruption and a resulting
glitch can have serious cascading effects on all supply chain members. Not
only a negative performance impact but also a negative economic impact.
Three different empirical studies of Hendricks and Singhal show the negative
effect of supply chain glitches (supply and demand mismatch) on operating
performance, on shareholder wealth and on long-term stock price [29–31].
They also found out that it does not matter who caused the glitch, what the
reason was or what industry a firm belongs to. The glitches are associated with
negative operating performance across the chain [31].
Supply chain risk management (SCRM) became a critical supply chain
management (SCM) discipline in the past due to the increasing number of
events causing supply chain disruptions and to lower the impact of such supply
chain glitches [35]. [21] have empirically shown the benefit of information
sharing of supply chain members to understand the different risks which could
have an impact on the supply chain [21]. Though a SC wide proactive risk
management based on risk related information transparency is required to
increase the security of supply, decrease safety stocks and to lower costs for
manufacturer and their customers [11, 14]. While supply chain risk (SCR)
information has been identified as crucial, the importance of a company’s
information processing capability to its SCRM effort has received little
attention in the literature [22]. [22] have shown in an empirical study the
positive effect on a firms operational performance by an improved capability
inprocessingsupplychainriskinformation,whichcomprisessupplychainrisk
information sharing and analysis [22]. The integration of modern technologies
into supply chains leads to a smart supply chain management, which combines
multiple independent data analytics models, historical data repositories, and
real-time data streams [75]. Through this embedded intelligence, supply
chain management moves from supporting decisions to delegating them and,
ultimately, to predicting which decisions need to be made [10]. Using this
available data from digitalized supply chain processes in SCRM leads to a
smart SCRM (SSCRM). A system which processes SCR information helps
firms to respond in a timely manner – however, there is a need to test whether
the benefits outweigh the implementation costs [23]. The problem at this

A Simulation-Based Evaluation Approach for Digitalization Scenarios 181
time is, none or only limited empirical/objective information about digital-
ization benefits is available – a method is needed which allows estimation
of costs and benefits of digitalization initiatives [4]. This paper presents
a simulation-based approach for assessing process oriented digitalization
scenarios (DS) as part of creating a SSCRM. The method should either
be used with expert opinions or objective information (when available) to
estimate potential benefits. Additionally a methodological framework will be
presented which helps practitioners to develop such DS based on a previous
SCR evaluation.
In Section 2, a research overview will be described. In Section 3 a
framework and its new assessment method will be developed, and applied at
a German steel producer in Section 4. The results will be shown in Section 5
and the paper ends with a final discussion and managerial implications in
Section 6.
2 Research Overview
2.1 Supply Chain Risk Management
SCRM can be seen as an emerging critical and cross-functional discipline
between SCM, corporate strategic management and Enterprise Risk Manage-
ment (ERM) [35, 81]. In the literature the definition of “supply chain risk” and
SCRM is not unified [18, 64]. According to [36], definitions of “supply chain
risk” can be found at [8, 19, 39, 72, 80]. Most of the proposed definitions do
not span across the entire chain [36]. Due to that, the authors of this paper
define supply chain risk accordingly to the work of [36] as “the likelihood
and impact of unexpected macro and/or micro level events or conditions that
adversely influence any part of a supply chain leading to operational, tactical,
or strategic level failures or irregularities”. Additionally the authors differ
between three supply chain risk elements in accordance to [37]: risk sources,
risk events and risk effects. Also according to [36], definitions of SCRM can
be found at [26, 38, 39, 51, 68, 70]. In their literature review, 36 [36] stated that
the proposed definitions focus on specific elements of SCRM and do not span
the SCRM processes completely or differ in their SCRM methods and types
of events. Given this, the authors also follow [36] in their definition of SCRM
as “an inter-organisational collaborative endeavour utilising quantitative and
qualitative risk management methodologies to identify, evaluate, mitigate
and monitor unexpected macro and micro level events or conditions, which
might adversely impact any part of a supply chain”. When a company

182 F. Schl¨uter et al.
knows its supply chain risks, principal SCRM strategies are available: avoid
(eliminate), reduce (removing the risk source, changing of likelihood and/or
consequences), transfer (by contract, insurance, physical transfer or sharing)
or accept (retain) the risks [49, 57].
2.2 Digitalization and Industry 4.0
The usage of available real time (risk) information throughout the SC can be
seen as one way of influencing a risk probability (avoiding) or risk impact
(reduce risk effects). It also makes risk transferring easier with the help
of smart contracts. The integration or creation of Cyber-Physical-Systems
(CPS) in existing or new supply chain processes leads to a convergence
of the physical world and the virtual world (process digitalization) [73].
“CPS integrate computation with physical processes, and provide abstractions,
modelling, design, and analysis techniques for the integrated whole” [74]
Beside the term digitalization there are other definitions in the literature
with a similar meaning [33]. Especially the term Industry 4.0 or Industrie
4.0 (I4.0) is widely used in German speaking literature and slowly makes
its way into Anglo-Saxon literature (Industrie 4.0 as well as Industry 4.0)
(e.g. [73] or [58]). [56] have performed a structured literature review and
have shown that the biggest impact from I4.0 technologies and concepts is
to be expected especially for the procurement, production and distribution
activities in the supply chain. They have built up their literature review on
articles which mostly can be found in scientific magazines and lower-rated
journals as well on studies published by companies or research institutes –
especially from the German literature [56]. This is because the whole topic of
industrial digitalization or I4.0 is still today an emerging, under-developed and
highly diverse research field [24, 56]. To give direction for practitioners and
researchers, “work-cluster” for transforming value creating activities into the
I4.0 are formalized within the “Dortmund Management-Model for Industry
4.0” by Henke [69]. More information about I4.0 and its components can be
foundintheliterature[3,6,69].Forthispaper,thetermdigitalizationisdefined
as a necessary condition on the road to I4.0 and will be used synonymously at
some points. The main drivers for the digitalization of supply chain processes
are typically an increase in flexibility and reaction rate of industrial/logistic
systems [69]. Another perspective is to improve the supply chain robustness
by using this available data from digitalized supply chain processes and CPS
in SCRM, leading to a smart SCRM. Making the supply chain smarter from
a risk management perspective can be described as “SCRM digitalization”,

A Simulation-Based Evaluation Approach for Digitalization Scenarios 183
thus as “the integration of technology (sensors, actors, connectivity, analytics)
along supply chain processes to improve supply chain risk identification,
analysis, assessment, mitigation and monitoring through processing real time
supply chain risk information – which comprises supply chain risk information
sharing and analysis” [59].
2.3 Contribution to the Research Field
In the literature, various frameworks for leading digitalization initiatives
in companies are available – all with a different focus and some lacking
methodological support for evaluating developed scenarios. General I4.0
frameworks with no relation to risk management or SCRM can be found
at [9, 33, 47, 61]. The principal structure of all frameworks is: (1) capturing
of the current situation, (2) derivation of scenarios for potential processes, (3)
evaluation of scenarios, and (4) road mapping. While the frameworks of [47]
and [9] have no evaluation phase, the framework of [33] uses a qualitative and
subjective scoring for assessing scenarios and the framework of [61] includes
earlier work of [62]. The developed Extended Performance Analysis (EPA)
of [62] links non-monetary and non-directly measurable digitalization effects
with monetary effects, over cause-and-effect chains. Their method will be
discussed at a later point in this paper. Due to its focus on monetary effects
of DSs it serves as a basis for the developed simulation-based evaluation
method. SCRM and digitalization related frameworks can be found at [41, 60].
[41] have developed a four-phase framework for digitalizing processes from
a risk management perspective. Their method has a focus on potential risks
through the implementation of digitalization technologies. There is also a
lack of evaluating the benefits of digitalization. Due to that, [60] have
developed a five-phase framework for process oriented digitalization leading
to SSCRM. The main weaknesses of their method are a superficial description
of suitable methods for the different framework phases, especially for “risk
digitalization” and “digitalization scenario evaluation”, and they focus only on
supply chain risk identification. However, due to its focus on process oriented
SCRM improvement through digitalization the framework of [60] serves as a
basis and will be enhanced with concrete methods and the simulation-based
evaluation method will be integrated into the framework (see next chapter).
Right now there is no known publication with a holistic method for SCRM DS
development and assessment, which focuses on improving SCRM in general
through digitalization. Due to the increasing globalization and complexity of
the network structures of companies, predicting various scenarios can make

184 F. Schl¨uter et al.
a valuable contribution for future planning [27]. In this context, a scenario is
understood as a representation of different futures [7]. Due to distrustful results
and the lack of quantitative data, a trend towards a combination of quantitative
and qualitative methods can be observed. The increasing dynamics of research
objects can no longer afford purely quantitative methods [27]. Thus in this
paper a hybrid methodology consisting of qualitative and quantitative aspects
will be developed.
3 Method Development
The overall process is based on a framework from [60], supplemented by
various research methods and extended for digitalizing SCRM in general
(Figure 1). They have combined major components of the planning process
whichtendtobefoundinmostplanningprocessmodels[1]andhaveoriginally
Figure 1 Framework for SCRM Digitalization.

A Simulation-Based Evaluation Approach for Digitalization Scenarios 185
made them up for a planning process for digitalizing only the supply chain
risk identification. The extended framework consists of six phases (Figure 1):
Process identification/specification; Risk identification; Risk analysis; Risk
evaluation; Risk digitalization and Digitalization scenario analysis.
3.1 Phase 1: Process Identification/Specification
At first target processes (TP) and risks (separated into risk elements like
sources, events and effects [37]) have to be identified using data collecting
methods like process observation, expert interviews and secondary company
data [53]. This is followed by a process visualization. Numerous models for
illustrating business processes have been developed in the past but based on
a comparative study only the process chain model of [43] was especially
developed for illustrating, designing and analysing logistic processes [52].
The basic element of this model is a general process chain element, which
is initially described by processes which help making goal oriented trans-
formation to an object [52]. Each process chain elements is influenced by
the internal structures, processes, resources and steering modes as well as
by the exchange with the environment via a source and a sink [52]. Each
element also has a set of key performance indicators (KPI) describing its state:
throughput time, technical capacity, work-in-progress, costs and schedule
adherence [43, 78]. Because a process chain element is always setup the
same, independent from the level of particularization, it can be constructed
for various levels of detail.An enterprise can be conceptualized en bloc as one
process chain element or it can be broken down into various highly detailed
levels [52]. The right level of detail must be estimated by the user. For this
work, the authors focus only on four of the five KPIs and thus will neglect the
schedule adherence for the following method. Based on the collected company
information a process chain plan of material flow, information flow and/or
financialflowcanbemodelledandusedfortheriskidentificationprocessinthe
next phase.
3.2 Phase 2: Risk Identification
The main goal of this phase is to find, recognize and describe risk elements in
terms of risk source, event and effect [37]. Typical approaches for identifying
risks for every defined process from the first phase are: risk checklists, expert
interviews and workshops (i.e. [28]). Which methods should be used depends
on the use case scale and also on how many risks should be identified. Some-
times it can be hard for experts to distinguish between the aforementioned

186 F. Schl¨uter et al.
risk elements (source, event, effect). In this case, it is practical to collect all
information from the interviewees and do clustering within the risk analysis of
the third phase.Also usually applied risk mitigation actions should be collected
for the later analysis.
3.3 Phase 3: Risk Analysis
In the next phase the processes and risk elements have to be put into relation
by creating cause-and-effect chains (CEC), based on the Fault-Tree logic (e.g.
[44]). The CEC will be constructed for each process and later be merged to
create an overall risk map. The information for the analysis has to be gathered
from the results of Phase 2. If the experts were not able to distinguish between
the three risk elements, a practical approach is an iterative analysis. The
moderator who collected the risk information in Phase 2 first tries to construct
CEC by himself and then brings the results into a discussion with the experts
to validate the results. When the moderator has to create the CEC it is useful
to start with potential risk sources. The risk sources have per definition no
predecessor risk element (attention: this statement is only valid while focusing
on only one process chain element – when creating the risk map later, risk
sources of one element could also have predecessor risk elements in other parts
of the process chain). After thinking about what of the experts descriptions
could be a source, the moderator has to think about what descriptions could be
possible events, resulting from these sources. In practice there is no limit of
risk events between risk sources and risk effects – it depends on the expert’s
descriptionlevelofdetail.Itisalsopossiblethatonesourcecanleadtodifferent
following events. After connecting risk effects and sources, the next step
is to select the corresponding mitigation actions mentioned by the experts.
The last step is to define the resulting effect on the process chain element,
depending on the chosen mitigation action. For this step, some clarifications
are necessary:
• The effects are measured on the aforementioned KPIs of the metho-
dology by [43] and whenever an object leaves the process chain
element [78].
• Also only the direct effects of a risk have to be considered and not
succeeding effects. For example: Due to a risk event a transport service
provider has less transport capacity available, which leads to a rise in
lead-time – in this case only the reduced capacity has to be considered,
because the rise in lead time will later automatically considered within
the simulation (risk evaluation).

A Simulation-Based Evaluation Approach for Digitalization Scenarios 187
• It is also possible that multiple effects occur. For example: Due to
bankruptcy of a service provider the company has to switch to another
provider which is more expensive and is more slowly in transportation –
in this case there are two parallel effects with a rise in cost and a rise in
lead-time which both have to be considered.
After creating the local CEC, they will be validated together with the experts
and changes have to be implemented. When all local CEC are validated they
will be connected and integrated into a global risk map. This should also
happeninaworkshoptogetherwithexpertstoeasilyidentifyriskdependencies
between process chain elements. The final risk map contains all observed
processes, related risks and inter-process risk dependencies.
3.4 Phase 4: Risk Evaluation
For the aggregated risk evaluation a Discrete Event Simulation (DES) will be
combined with a Monte-Carlo-Simulation (MCS), based on a methodology
by [78]. Also other simulation types should work, like System Dynamics, as
long as the risk value distribution can be implemented via a MCS. The DES
simulates the regular material flow, based on the information from Phase 1
and the MCS is responsible for the consideration of risks and allows for a
supply chain risk evaluation based on the Loss-Distribution-Approach (LDA)
[63]. The probability of occurrence for each initial risk source and the impact
for its corresponding risk effect(s) has to be quantified. For the LDArisks will
be quantified in terms of probability distributions. Based on operational risk
literature [63] there are typically three sources to use for risk quantification: (1)
company data; (2) external data and (3) expert knowledge (self-assessment).
Self-assessment should be used if there is no or insufficient data available.
In this case the approach by [71] is recommended. At first experts will be
asked how often a certain risk occurs (during a time period or during order
processing – depends on the case). Based on the literature [15], if a risk occurs
only once within a given period, the probability of occurrence can assumed
to be Binomial distributed – otherwise Poisson distributed. Second, the risk
impacthastobequantified.Duetothesuperficialdescriptionin[78]andalsoin
[71] the authors suggest the following approach for risk impact quantification
(discussed with risk management experts in academia and approved in the case
study): To fit expert knowledge to probability distributions usable within the
simulation, the statistics software “R” is suggested, together with the package
“rriskDistributions” [5]. The package helps to identify a suitable distribution
based only on two quantile values gathered from the experts. Usually company

188 F. Schl¨uter et al.
experts are no experts in statistics thus are unable to give detailed information
aboutriskdistributions[65]–howevertheyareabletogiveatleastinformation
about the median risk impact (happens in 50% of the cases) and the pretty much
worst impact they have experienced (considered as the 95% or 99% quantile).
After selecting an appropriate distribution, the software will also give the user
the relevant parameters for the simulation. The described approach allows
assessing the impact of multiple risks at the same time on single company
process KPIs as well their impact on the whole system. At the end of Phase 4
the current situation is clear – the crucial supply chain processes and risk
sources have been identified.
3.5 Phase 5: Risk Digitalization
In Phase 5 DSs for risk containing processes have to be drafted. For this a
methodology by [9] will be used. Their work describes a meta-model for the
transformation of organizations towards I4.0 and combines existing change
management processes [46] with identified I4.0 design principles [33] to
develop DSs [9]. Their approach gives no information about the qualification
and number of participates in the workshops for risk digitalization. However,
these two aspects are decisive for the quality of the workshop results. It is
important that both experts from in the research field, manager as well as
creative employees from the shop floor participate in the workshop. The
number of participants should not be too high, as otherwise the creativity
of each individual will suffer, and the opinion leader of the group usually
prevails. If more participants are desired in the workshops, they should be
divided into several small groups [34]. Furthermore, the developed method-
ology by [9] does not mentioned the summary and documentation of the
workshop results. However, since this is important for the presentation of
the consensus of the group participants and the analysis of the DS, the
authors suggest a proposal in form of a profile with different categories.
The digitalization profile categories (DPC) for SCRM are explained in the
following:
• Process name, participant and objective (DPC1): In this category, the
focused process with its objective is stated and which organizational
areas and employees are involved.
• Identified risks (DPC2): The risks that relate to the focused process are
listed in this category. Thereby the identified risks from Phase 2 are
considered.

A Simulation-Based Evaluation Approach for Digitalization Scenarios 189
• Target situation with I4.0 technologies (DPC3): Technologies according
to I4.0, which are used in the new process flow, are summarized
in this category and how they are related to the identified risk
elements.
3.6 Phase 6: Digitalization Scenario Analysis
The approach of [9] as well as the framework of [60] lacks a methodological
support for evaluating the benefits of such scenarios especially with regard
to SCRM DSs. To overcome this lack, a hybrid qualitative-quantitative
approach has been developed. It merges previous ideas of [62] who have
developed a method called Extended Performance Analysis (EPA) for eval-
uating RFID investments with the described risk assessment method from
Phase 4. [62] link non-monetary and non-directly measurable digitalization
effects through RFID implementation with monetary effects, over CECs.
Because the risk evaluation and scenario development happens along SC
processes, the DS assessment should follow this approach to be consistent.
With this requirement, the method of [62] needs several extensions. A further
critique is that the authors have used CECs with subjective assumptions
regarding the interdependencies between the initial (non-monetary and non-
directly measurable) process-effects of DSs and propagating (non-monetary
but directly measurable, or monetary) effects. [62] also only use monetary
values to describe the effects which is not sufficient in the context of I4.0 and
SSCRM. To make it more suitable for the framework, this paper combines
their approach with a DES model. DES is recognized in the literature as a
valuable tool to test proposed strategies in an unpredictable environment (e.g.
multi-tiered supply chains) [76]. By using a DES, each DS can be evaluated
dynamically in the simulation and the (validated) simulation model objectifies
the CECs due to its close to reality behaviour. Instead of assuming all effects
and correlations the simulation will use the (predicted or known) initial effects
of a scenario (i.e. less breakdowns due to more information transparency)
and simulate/calculate the propagating effects accordingly to the defined and
validated simulation rules (i.e. reduced unplanned maintenance stops and thus
reduced maintenance costs and less delay) (Figure 2). Additionally a Monte
Carlo Simulation (MCS) is implemented, which helps to diminish the inherent
uncertainty in the decision making process and achieves results closer to
reality [79]. For the MCS experts will guess in workshops a parameter range
for each potential initial effect on specific processes and their risks, define
a probability distribution and the MC samples the possible combinations

190 F. Schl¨uter et al.
Figure 2 Comparison between original and simulation-based evaluation approach.
of parameters in proportion to their probability of occurring [48]. By com-
bining a DES with the EPA, a process oriented assessment method for DSs
(Phase 5) has been developed, which delivers less subjective results due to
the defined simulation rules and the implemented MCS for the initial effects.
The developed method also allows to assess DSs simultaneously with risks,
which will not be directly affected by the DS and also with potential risks
arising from the SC digitalization. The output comparison serves as a decision
support.
4 Framework Application at a German Steel Producer
The method validation took place at a German steel producer. Continuously
growing service expectancy and increasingly complex customer requirements
for individualization present new challenges for companies in the age of digi-
talization [42]. Particularly the companies in the German steel industry are in
demanding times. Produced overcapacities especially of China and declining
sales on world market caused decreasing sales prices, resulting in sales losses
aswellasvaluedeclineinwarehousesforsteelcompanies[25,77].Steelcanbe
seen as the most important engineering material in use today and its versatility,
strength, toughness, low cost and wide availability are unmatched [2, 16, 54].
So steel producers are an inseparable part of several complex supply chain
networks and they deliver key resources for different manufacturing industries
[60]. The steel industry is typically characterized through volatile lead times,
leading to problems with on-time-delivery, expensive ad-hoc solutions and
ultimately in sales losses. Risks such as failure of transport and/or work aids
in the delivery process lead to significant problems with the customer [38]. In
order to meet the market challenges in terms of price, quality and delivery

A Simulation-Based Evaluation Approach for Digitalization Scenarios 191
service, producers in the steel sector have to be proactive in identifying
potential risks in the supply chain. Out of this, the high consideration relevance
of steel producers is justified. The digitalization of processes in supply chains
assures to face the current challenges. For this reason, it is important to reduce
potential supply chain risks for the improvement of lead time fluctuation and
customer’s satisfaction.
4.1 Process Identification/Specification
Critical supply chain processes have been identified within expert interviews
[53]. For simplification, the paper focuses on one process, involving the
transport of cold strips (coils) to succeeding aggregates. Modelling is carried
out accordingly to the process chain model of [43]. Corresponding to [43]
processes can be characterized by five different process condition measures:
lead time, capacity (resources), inventory, on-time delivery and processing
costs [43]. The hot strip is stained and rolled by a cold rolling mill before the
coils are transferred to an intermediate buffer point by an automated conveying
line.An overhead crane picks up, transports and stores the coils in a warehouse.
An external transport service provider transports the coils to the next location.
The removal and loading are carried out by the same crane (Figure 3).
4.2 Risk Identification
In accordance to [28], the process owner were asked in interviews about a
representative/important sub-process and its risks. The process owner has
identified the “Pick-up, transport and storage coil via overhead crane” as the
TP because the highest risk in this transportation scenario is the failure of the
overhead crane. Consequently, the following aggregates cannot be supplied
due to a rise in lead time. After choosing a representative process they were
asked about possible risks. Because it was difficult for the process owners to
differentiate between risk sources, events and effects as well as to stay focused
Figure 3 Identified and modelled supply chain process.

192 F. Schl¨uter et al.
on only the TP they were asked to tell anything regarding risks and possible
mitigation actions for the overall process. For this example relevant mentioned
risk elements were:
• Crane unavailable • Delay
• Staff shortage • Waiting for substitute personnel
• Crane defect • Waiting for reparation
4.3 Risk Analysis and Evaluation
The overall risk analysis and evaluation approach are based on work by [78]
and [45]. The authors put all risk elements (source, event and effect) into
relation and validated the CECs with process owners (Figure 4). Due to the
strict focus on the mentioned TP and for simplification, all risk elements with
a relation to other processes have been neglected. In the defined TP the two
identified inherent risk sources have in the end a risk effect on the throughput
time of the TP, due to the chosen mitigation action (Figure 4).
After creating the CEC a single risk assessment took place. Therefore
historical data from a six months period in 2016 was available and helped
to determine both risk probabilities ([number of directly affected orders]/[all
orders during this period]). For both risk impacts no data was available and
experts were asked. The above-described method for transforming expert
knowledge into distributions has been used (see Section 3.4). For each impact
on the process condition measures the experts estimated a median value (lower
bound, mentioned in Figure 4) and a worst case value (upper bound, mentioned
in Figure 4) and a distribution and its parameter values for the simulation have
been derived.
Afterwards the described scenario has been modelled in a DES, combined
with a MCS [78]. Typically, a MCS is used to test risky scenarios and their
consequences in a Value-at-Risk framework [17, 66]. In this publication it
is shown how the identified risks and also later the DS will affect the mean
lead time. Thus the risk and SCRM DS assessment KPI will be Mean Lead
time-at-Risk. After validating and running the simulation the results represent
an aggregated evaluation of the risk affected current situation. For better
comparison, the results can be found in Section 5.
4.4 Risk Digitalization
In order to counteract the identified risks, a DS for the described transport
process has been developed with a focus on minimizing risk by using I4.0
technologies. For the creation of the DS, the developed design principles

A Simulation-Based Evaluation Approach for Digitalization Scenarios 193
Figure 4 Cause-and-Effect relationships of risk sources, events and effects.
by [33] are used in a workshop accordingly to the methodology by [9]. Two
groups, each with six workshop participants were formed. The group members
were both process owners from the departments of production, warehousing
and transportation (DPC1) as well as employees from a research institute to
promote the flow of creativity on the subject of I4.0. Each group developed
ideas to a DS. The results were combined into a scenario, which addresses
the identified process risks and reduces their impact. Due to the limited
focus on the TP (DPC1), only the relevant details of the scenario will be
outlined. The main purposes were to reduce problems with staff shortage
and sudden crane defects happening in the past (DPC2). In the DS, the
coil is transported and stored by an autonomous overhead crane, equipped
with predictive maintenance capabilities. The data for the storage location

194 F. Schl¨uter et al.
Figure 5 Extended CEC with estimated changes in risk values.
will be transferred wirelessly from the databases to the crane and real-time
status information from the crane sensors will be transferred back into the
database. All data is gathered and analysed by Big Data analytics, which
allows identification of unwanted system patterns in real-time (DPC3). The
documented scenario elements have been implemented in the CEC for better
assessment of the potential effects and to show additional risks (Figure 5).
4.5 Digitalization Scenario Analysis
The simulation is an important and common method, particularly in the context
of digitalization or I4.0. It can be used as an auxiliary tool to evaluate different
scenarios without realizing them. This allows predictions and can increase

A Simulation-Based Evaluation Approach for Digitalization Scenarios 195
decision support [40]. The initial effects of a DS are probably not completely
determined quantitatively at the time of evaluation (uncertainty exists) –
experts mostly will have to guess how a scenario will become manifest in
the initial effect numbers. For the MCS experts have guessed in workshops a
parameter range for the potential initial effect on each risk (Figure 5). Later
these ranges have been implemented into the simulation model.
Probabilities:As mentioned, there is no scenario data available and experts had
to estimate the DS impact on the parameters. Because of the missing data the
authors decided to use a triangle distribution for the probabilities, accordingly
to the literature [20]. To reflect the sceptical attitude of the experts towards
digitalization, the mean value has been defined only 1% better than the max.
(Worst-case) value (mean value in brackets in Figure 5).
Impacts: For the DS, the personnel shortage risk is not relevant anymore but
an additional risk of defect of the cranes smart capabilities arises. The experts
estimated this value based on their experience regarding IT system defects
of other machines. The reduction of the “crane defect” impact results from
the fact that often times half of the downtime comes from contacting and
waiting for the reparation equipment/team to appear. With smart capabilities
the team can be informed when the system detects an imminent defect. The
distributions have been derived based on the same method described in the
risk assessment phase.
The input data and simulation results of the DS can also be found in the
next section.
5 Simulation Results
Before the actual evaluation took place, the number of MCS replications
had to be determined. Therefore, the simulation results for 10, 100, 200,
500, 750 and 1000 replications have been compared. Because there was no
significant change in the results after 500 runs for this small case, the number
of replications for the evaluation had been set to 500. This approach goes
along with the literature [50]. With the collected risk input values in Table 1
the simulation has been run for a risk-free, current and digitalization scenario.
The results can also be found in Table 1.
The results show that even the expert’s conservative expectations of the
DS can have a mitigating effect on the rise in lead time through risk. Within the
DS the Mean Lead time-at-risk has not been higher than 7893 sec. (132 min.),
with a 95% confidence level. The results can be used subsequently to calculate

196 F. Schl¨uter et al.
Table 1 Simulation input and results
No Risk Current Scenario
Staff shortage Probabilty (%) 0 15% 0
Impact (Min.) 0 20–60 0
CPS defect Probabilty (%) 0 0 1%–10% (9%)
Impact (Min.) 0 0 50–90
General defect Probabilty (%) 0 10% 1%–9% (8%)
Impact (Min.) 0 60–240 30–120
Mean Lead time-at-risk (Sec.) 7680 8393 7893
Mean Lead time-at-risk (Min.) 128 140 132
Change – 9% 3%
the positive financial impact of the DS, which in return can be compared with
expected investment costs.
6 Conclusion and Further Research
The paper presents a flexible simulation-based approach for assessing process
oriented DSs based on customizable KPIs and prior to realization, which can
either be used with expert estimations or objective information to estimate
potential benefits. The assessment approach is integrated into a framework
and its applicability could be validated within a case study of a German steel
producer. Asaresult,theexpectedMeanLeadtime-at-risk couldbequantified.
The positive evaluation helped selecting the scenario for implementation and
it shows the positive effect of digitalization on SCRM.
6.1 Limitations and Further Research
When applying the framework, the user has to be aware about its limita-
tions. The simulation-based evaluation approach probably needs more user
competence regarding DES and it needs time to complete all steps of the
framework. There is a need in comparing the proposed method and the
original approach by [62] regarding necessary effort and result accuracy.
Due to the positive scenario evaluation at the German steel producer and
a following realization, the shown case study can serve as a pilot study to
benchmark subsequent studies on the implemented DS. A comparison of the
pilot study with the subsequent study results (predicted benefit vs. real benefit)
allows further research on the reliability of either the expert predictions or the
proposed evaluation method in general. This helps to improve the proposed
method and evaluate its usefulness. Another potential for further research

A Simulation-Based Evaluation Approach for Digitalization Scenarios 197
is an improved method for DS development within the context of SCRM.
The suggested transformation method by [9] served as a basis for industry
workshops at the German steel producer. Due to a superficial description
of how such transformation workshops should be set up and proceed, the
authors had to improvise beforehand and during the workshop. The developed
method has been used for assessing process oriented SCRM DSs. Further
research is required to find out if the approach can be used for assessing
process oriented DSs with no focus on SCRM. For example: the evaluation
of potentials in collaborative planning and control processes in supply chains
through digitalization. There is also a need in identifying suitable approaches,
formalizing the impact estimation of digitalization effects on risk parameters.
It is also worthwhile to test the presented approach with other types of
simulations then DES. Especially System Dynamics can be an appropriate
tool for larger problems because of better runtime performance and when the
evaluation shall be more strategic and less detailed.
6.2 Managerial Implications
The described framework allows practitioners to derive individual and process
oriented DSs for a smart SCRM, and the subsequent assessment of such
scenarios. The smart SCRM mentioned here is a good basis for a proactive
SCRM which in the literature is discussed on a conceptual basis for many years
(e.g. [32]) but up to now it has rarely been realised in business practice. In
the age of Big Data, digitisation and autonomisation today we have sufficient
data as well as the technologies (such as Blockchain), which can allow a
proactive management of such data along supply chains. The transparency
in value-added networks exists end-to-end so that in the future risks can be
avoided or reduced at an earlier stage than today. For a practical application
of such a SSCRM it is also necessary that there is a structured approach from
application-oriented research to core elements of a cycle of SSCRM.
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Biographies
F. Schl¨uter is a Ph.D. student in the Graduate School of Logistics at the
Technical University of Dortmund, Germany, since summer 2015. He received
his B.Sc. and M.Sc. in Industrial Engineering from the Technical University
of Dortmund and completed a semester abroad at the Michigan Technological
University, in Houghton, Michigan, USA. Parallel to his Bachelor’s studies
he was working for the Institute of Production Systems in Dortmund and
completed internships in the electrical industry and automobile industry.
During the last year of his Master’s program he was working for a management
consulting company, with focus on Operations and Supply Chain projects. His
research focus is on the digitalization of Supply Chains and Supply Chain Risk
Management, in collaboration with thyssenkrupp Steel Europe AG.
E. Hetterscheid is a Ph.D. student in the Graduate School of Logistics at
the Technical University of Dortmund since summer 2016. He received his
B.Sc. and M.Sc. in Industrial Engineering from the Technical University

A Simulation-Based Evaluation Approach for Digitalization Scenarios 205
of Dortmund. During his studies he worked for the Institute of Production
Systems and the Fraunhofer Institute for Material Flow and Logistics in
Dortmund. He completed national and international project internships in
the area of Lean Management and Six Sigma. Currently, he is completing
his doctor degree in cooperation with thyssenkrupp Steel Europe AG. His
Ph.D. work focusses on the development of new approaches for collaborative
planning and control in supply chains in the context of Industry 4.0.
M. Henke completes the board of directors of Fraunhofer IML as new
director of the section Enterprise Logistics and he also holds the chair of
Enterprise Logistics at the faculty of Mechanical Engineering atTU Dortmund
University. His research foci are, among others, the area of management
of the Industry 4.0, purchasing and supply management, supply chain risk
management and financial supply chain management.
Michael Henke began his carrier studying Brewing and BeverageTechnol-
ogyattheTechnicalUniversityofMunich(Dipl.-Ing.).Hegainedhisdoctorate
and habilitation in Business and Economics at the Technical University of
Munich. Henke held the Chair of Purchasing and Supply Management at EBS
European Business School in Wiesbaden from 2007 to 2013. During the last
year of his habilitation Michael Henke was also working as senior consultant
for the Supply Management Group SMG in St. Gallen, Switzerland.